Big Data: Predictive Analytics in the Retail Industry

Big Data and Retail: The Basics

Big data is a trend that has manifested within the past few years and sparked a small revolution in how retailers market to their potential consumer base. Its influence has inspired numerous headlines and stories since its introduction, the most famous being the story in which Target predicted a teenager was pregnant before her family knew. The impact of big data on the future of marketing and the industry as a whole is being amplified as companies become smarter about how to apply the data.

Big data as a concept is fairly straightforward. In general, big data refers to data or information in sets too large for conventional methods to process, and in a retail context it is usually focused on market data, habits, and especially consumers. Broken down into its two defining concepts, big data is 1) large quantities of data that 2) can’t be processed like it’s been done in the past.

When people talk about big data, they are usually referring to information centred around the habits of consumers the retailer is interested in attracting. Information is gathered that ranges from the customer’s race, address and family status to their shopping habits, brand allegiances and other tendencies that can be quantified or tracked. Of most interest to retail professionals in particular is the target’s recent purchases. From there, the retailer’s analysts can compare similar purchasing patterns historically and act from there, as Target did with their pregnancy campaign.

However, big data doesn’t necessarily need to be exclusive to the realm of marketing (although marketing will likely always play at least a minor role). For example, Costco has been known to use big data insights to contact customers who purchased a recalled item directly from one of their stores. The message reiterated the product recall and invited the customer to return the product for a full refund. Extra warning (above and beyond what was required by law) was appreciated by customers and only helped increase loyalty and trust, fostering positive public opinion.

Using Big Data to Predict Retail Trends — and Execute on Them

One important application of big data revolves around predicting trends and forecasting market demand. A good example of how this would be done can be found here. The angle taken here features a broader scope of information than marketing in order to predict what will be popular, and from there how to best capitalize on the demand.

In the video game example, the ambitious retailer decides they want to dominate the next game release season through leveraging big data. The first thing they need to do is to look at a variety of factors to figure out what the next big trend will be. This prediction features web browsing habits, social media reaction, advertising patterns, and even some other miscellaneous factors that have been related to popularity in the past.

Next, the retailer has to find out where they’re going encounter the most demand. They do this through previous transaction history, shopping patterns, and demographic data to find out which stores or regions will be the all-stars. From there, it’s a matter of making sure you’re stocking, marketing and offering promotions as best as possible as the product launches happen and the trend takes shape.

Closing Thoughts

Of course, most people would probably feel a little skeptical at the use of big data in making predictions, and at the accuracy of large market predictions as a whole. A lot of the time, the doubt is justified, but that’s part of the beauty of big data. There is an extremely large sample size at play here and that means predictions tend to be more accurate.Big data has a number of other miscellaneous applications. It can be used to figure out the most competitive price point, make better recommendations, and predict the needs of your customers as the demographic shifts throughout the day. Big data identifies your all-star customers, allowing specific targeting for the purposes of retaining their loyalty. Finally, you can track spending habits on a more macro level. Are two seemingly unrelated products sold together often? Will moving them closer together or increasing visibility increase sales?

One last thing that’s worth mentioning: if you’re a regular consumer being targeted by retailers through big data, there’s a really high chance you’re going to be creeped out. The Target pregnancy story is a good example of this, and the big brother-esque factor is one of the reasons that it got so much attention. If you’re a marketer, you need to find a way to leverage your findings without making it obvious that you’re tracking your customer.

While most vendors don’t have the resources to conduct big data analysis, it may be worth a try to ask your buyer how the retailer is leveraging the data. It can’t hurt to appear more knowledgeable about higher level trends your retailer cares about.

The big data phenomenon illustrates the value having lots of information can have towards your bottom line, giving insights and opening avenues you may have had no idea existed. The norm is shifting, and leveraging data is becoming more and more necessary for success in the industry.